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# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from huggingface_hub import hf_hub_download
from functools import lru_cache
from offline_asr import OfflineAsr
sample_rate = 16000
@lru_cache(maxsize=30)
def get_pretrained_model(repo_id: str) -> OfflineAsr:
if repo_id in chinese_models:
return chinese_models[repo_id](repo_id)
elif repo_id in english_models:
return english_models[repo_id](repo_id)
elif repo_id in chinese_english_mixed_models:
return chinese_english_mixed_models[repo_id](repo_id)
else:
raise ValueError(f"Unsupported repo_id: {repo_id}")
def _get_nn_model_filename(
repo_id: str,
filename: str,
subfolder: str = "exp",
) -> str:
nn_model_filename = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder,
)
return nn_model_filename
def _get_bpe_model_filename(
repo_id: str,
filename: str = "bpe.model",
subfolder: str = "data/lang_bpe_500",
) -> str:
bpe_model_filename = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder,
)
return bpe_model_filename
def _get_token_filename(
repo_id: str,
filename: str = "tokens.txt",
subfolder: str = "data/lang_char",
) -> str:
token_filename = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder,
)
return token_filename
@lru_cache(maxsize=10)
def _get_aishell2_pretrained_model(repo_id: str) -> OfflineAsr:
assert repo_id in [
# context-size 1
"yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12", # noqa
# context-size 2
"yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12", # noqa
]
nn_model_filename = _get_nn_model_filename(
repo_id=repo_id,
filename="cpu_jit.pt",
)
token_filename = _get_token_filename(repo_id=repo_id)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=None,
token_filename=token_filename,
sample_rate=sample_rate,
device="cpu",
)
@lru_cache(maxsize=10)
def _get_gigaspeech_pre_trained_model(repo_id: str) -> OfflineAsr:
assert repo_id in [
"wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2",
]
nn_model_filename = _get_nn_model_filename(
# It is converted from https://huggingface.co/wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2 # noqa
repo_id="csukuangfj/icefall-asr-gigaspeech-pruned-transducer-stateless2", # noqa
filename="cpu_jit-epoch-29-avg-11-torch-1.10.0.pt",
)
bpe_model_filename = _get_bpe_model_filename(repo_id=repo_id)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=bpe_model_filename,
token_filename=None,
sample_rate=sample_rate,
device="cpu",
)
@lru_cache(maxsize=10)
def _get_librispeech_pre_trained_model(repo_id: str) -> OfflineAsr:
assert repo_id in [
"csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13", # noqa
]
nn_model_filename = _get_nn_model_filename(
repo_id=repo_id,
filename="cpu_jit.pt",
)
bpe_model_filename = _get_bpe_model_filename(repo_id=repo_id)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=bpe_model_filename,
token_filename=None,
sample_rate=sample_rate,
device="cpu",
)
@lru_cache(maxsize=10)
def _get_wenetspeech_pre_trained_model(repo_id: str):
assert repo_id in [
"luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2",
]
nn_model_filename = _get_nn_model_filename(
repo_id=repo_id,
filename="cpu_jit_epoch_10_avg_2_torch_1.7.1.pt",
)
token_filename = _get_token_filename(repo_id=repo_id)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=None,
token_filename=token_filename,
sample_rate=sample_rate,
device="cpu",
)
@lru_cache(maxsize=10)
def _get_tal_csasr_pre_trained_model(repo_id: str):
assert repo_id in [
"luomingshuang/icefall_asr_tal-csasr_pruned_transducer_stateless5",
]
nn_model_filename = _get_nn_model_filename(
repo_id=repo_id,
filename="cpu_jit.pt",
)
token_filename = _get_token_filename(repo_id=repo_id)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=None,
token_filename=token_filename,
sample_rate=sample_rate,
device="cpu",
)
chinese_models = {
"yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12": _get_aishell2_pretrained_model, # noqa
"yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12": _get_aishell2_pretrained_model, # noqa
"luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2": _get_wenetspeech_pre_trained_model, # noqa
}
english_models = {
"wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2": _get_gigaspeech_pre_trained_model, # noqa
"csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13": _get_librispeech_pre_trained_model, # noqa
}
chinese_english_mixed_models = {
"luomingshuang/icefall_asr_tal-csasr_pruned_transducer_stateless5": _get_tal_csasr_pre_trained_model, # noqa
}
all_models = {
**chinese_models,
**english_models,
**chinese_english_mixed_models,
}
language_to_models = {
"Chinese": sorted(chinese_models.keys()),
"English": sorted(english_models.keys()),
"Chinese+English": sorted(chinese_english_mixed_models.keys()),
}
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